library(readr)
etf4_csv<-read_csv("ETF4_2000_2018_d.csv", locale = locale(encoding='big5'))
## Parsed with column specification:
## cols(
## 證券代碼 = col_character(),
## 簡稱 = col_character(),
## 日期 = col_double(),
## `未調整收盤價(元)` = col_double(),
## `當日均價(元)` = col_double()
## )
head(etf4_csv)
## # A tibble: 6 x 5
## 證券代碼 簡稱 日期 `未調整收盤價(元)` `當日均價(元)`
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 0050 元大台灣50 20090105 34.2 34.3
## 2 0056 元大高股息 20090105 13.9 14
## 3 0050 元大台灣50 20090106 34.2 34.2
## 4 0056 元大高股息 20090106 14.0 14.0
## 5 0050 元大台灣50 20090107 34.6 34.6
## 6 0056 元大高股息 20090107 14.3 14.3
str(etf4_csv)
## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 7493 obs. of 5 variables:
## $ 證券代碼 : chr "0050" "0056" "0050" "0056" ...
## $ 簡稱 : chr "元大台灣50" "元大高股息" "元大台灣50" "元大高股息" ...
## $ 日期 : num 20090105 20090105 20090106 20090106 20090107 ...
## $ 未調整收盤價(元): num 34.2 13.9 34.2 14 34.6 ...
## $ 當日均價(元) : num 34.3 14 34.2 14 34.6 ...
## - attr(*, "spec")=
## .. cols(
## .. 證券代碼 = col_character(),
## .. 簡稱 = col_character(),
## .. 日期 = col_double(),
## .. `未調整收盤價(元)` = col_double(),
## .. `當日均價(元)` = col_double()
## .. )
etf4.c<-etf4_csv[, c(-2, -4)]
etf4.c<-etf4.c[-1,]
colnames(etf4.c)<-c("id", "date", "price")
library(reshape2)
etf4.reorder = dcast(etf4.c, date~id)
## Using price as value column: use value.var to override.
etf4.reorder$date<-as.Date(as.character(etf4.reorder$date), "%Y%m%d")
dim(etf4.reorder)
## [1] 2474 5
head(etf4.reorder)
## date 0050 0056 006205 00646
## 1 2009-01-05 NA 14.00 NA NA
## 2 2009-01-06 34.21 14.02 NA NA
## 3 2009-01-07 34.59 14.28 NA NA
## 4 2009-01-08 33.21 13.86 NA NA
## 5 2009-01-09 32.32 13.61 NA NA
## 6 2009-01-10 31.91 13.55 NA NA
str(etf4.reorder)
## 'data.frame': 2474 obs. of 5 variables:
## $ date : Date, format: "2009-01-05" "2009-01-06" ...
## $ 0050 : num NA 34.2 34.6 33.2 32.3 ...
## $ 0056 : num 14 14 14.3 13.9 13.6 ...
## $ 006205: num NA NA NA NA NA NA NA NA NA NA ...
## $ 00646 : num NA NA NA NA NA NA NA NA NA NA ...
library(xts)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
etf4.xts<-xts(etf4.reorder[,-1], order.by = etf4.reorder$date)
head(etf4.xts)
## 0050 0056 006205 00646
## 2009-01-05 NA 14.00 NA NA
## 2009-01-06 34.21 14.02 NA NA
## 2009-01-07 34.59 14.28 NA NA
## 2009-01-08 33.21 13.86 NA NA
## 2009-01-09 32.32 13.61 NA NA
## 2009-01-10 31.91 13.55 NA NA
tail(etf4.xts)
## 0050 0056 006205 00646
## 2018-12-22 74.75 24.15 25.08 22.93
## 2018-12-24 74.67 24.16 25.25 22.72
## 2018-12-25 73.57 23.90 24.90 22.51
## 2018-12-26 73.87 23.83 25.16 22.13
## 2018-12-27 74.81 23.96 25.30 22.95
## 2018-12-28 75.21 23.92 25.24 23.16
str(etf4.xts)
## An 'xts' object on 2009-01-05/2018-12-28 containing:
## Data: num [1:2474, 1:4] NA 34.2 34.6 33.2 32.3 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:4] "0050" "0056" "006205" "00646"
## Indexed by objects of class: [Date] TZ: UTC
## xts Attributes:
## NULL
library(quantmod)
## Loading required package: TTR
## Version 0.4-0 included new data defaults. See ?getSymbols.
etf4_monthly <- to.monthly(etf4.xts, indexAt = "lastof", OHLC=FALSE)
## Warning in to.period(x, "months", indexAt = indexAt, name = name, ...):
## missing values removed from data
head(etf4_monthly)
## 0050 0056 006205 00646
## 2015-12-31 60.75 21.81 31.97 20.07
## 2016-01-31 59.51 21.48 26.21 19.21
## 2016-02-29 61.25 22.39 25.90 19.22
## 2016-03-31 64.82 22.44 27.83 19.73
## 2016-04-30 61.63 21.58 27.17 19.82
## 2016-05-31 63.10 21.86 27.10 20.31
library(PerformanceAnalytics)
##
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
##
## legend
library(magrittr)
etf4_returns_xts <-Return.calculate(etf4_monthly, method = "log") %>%
na.omit()
head(etf4_returns_xts)
## 0050 0056 006205 00646
## 2016-01-31 -0.02062272 -0.01524631 -0.198656946 -0.0437951860
## 2016-02-29 0.02881948 0.04149216 -0.011898049 0.0005204268
## 2016-03-31 0.05665035 0.00223065 0.071871607 0.0261889165
## 2016-04-30 -0.05046543 -0.03907812 -0.024001152 0.0045512089
## 2016-05-31 0.02357200 0.01289152 -0.002579696 0.0244218467
## 2016-06-30 0.03335201 0.03196311 -0.017119882 -0.0249265149
dim(etf4_returns_xts)
## [1] 36 4
plot(etf4_returns_xts, xaxt='n')
axis(1, index(etf4_returns_xts), format(index(etf4_returns_xts), "%Y/%m"))
library(ggplot2)
etf4_ret.df1<-fortify(etf4_returns_xts)
head(etf4_ret.df1)
## Index 0050 0056 006205 00646
## 1 2016-01-31 -0.02062272 -0.01524631 -0.198656946 -0.0437951860
## 2 2016-02-29 0.02881948 0.04149216 -0.011898049 0.0005204268
## 3 2016-03-31 0.05665035 0.00223065 0.071871607 0.0261889165
## 4 2016-04-30 -0.05046543 -0.03907812 -0.024001152 0.0045512089
## 5 2016-05-31 0.02357200 0.01289152 -0.002579696 0.0244218467
## 6 2016-06-30 0.03335201 0.03196311 -0.017119882 -0.0249265149
plot(etf4_ret.df1$`0050`, etf4_ret.df1$`00646`, pch=20,
col = 'darkred', main = '0050 vs. 00646 monthly returns',
xlab = '0050', ylab = '00646 S&P500')
library(tidyverse)
## -- Attaching packages ------------------------------------- tidyverse 1.2.1 --
## √ tibble 2.0.1 √ dplyr 0.8.0.1
## √ tidyr 0.8.3 √ stringr 1.4.0
## √ purrr 0.3.1 √ forcats 0.4.0
## -- Conflicts ---------------------------------------- tidyverse_conflicts() --
## x tidyr::extract() masks magrittr::extract()
## x dplyr::filter() masks stats::filter()
## x dplyr::first() masks xts::first()
## x dplyr::lag() masks stats::lag()
## x dplyr::last() masks xts::last()
## x purrr::set_names() masks magrittr::set_names()
library(ggplot2)
library(lubridate)
##
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
##
## date
etf4_ret.df2 <- cbind(etf4_ret.df1, month=month(index(etf4_returns_xts)),
year=year(index(etf4_returns_xts)))
ggplot(data = etf4_ret.df2) +
geom_point(mapping = aes(x = etf4_ret.df2$`0050`, y = etf4_ret.df2$`0056`, color = month))
ggplot(data = etf4_ret.df2) +
geom_point(mapping = aes(x = etf4_ret.df2$`0050`, y = etf4_ret.df2$`0056`, size = month))
ggplot(data = etf4_ret.df2) +
geom_point(mapping = aes(x = etf4_ret.df2$`0050`, y = etf4_ret.df2$`0056`, alpha = month))
etf4_ret<-coredata(etf4_monthly[-1,])/coredata(etf4_monthly[-dim(etf4_monthly)[1],])-1
head(etf4_ret)
## 0050 0056 006205 00646
## [1,] -0.02041152 -0.01513067 -0.180168908 -0.0428500249
## [2,] 0.02923878 0.04236499 -0.011827547 0.0005205622
## [3,] 0.05828571 0.00223314 0.074517375 0.0265348595
## [4,] -0.04921321 -0.03832442 -0.023715415 0.0045615813
## [5,] 0.02385202 0.01297498 -0.002576371 0.0247225025
## [6,] 0.03391442 0.03247941 -0.016974170 -0.0246184146
class(etf4_ret)
## [1] "matrix"
etf4_ret
## 0050 0056 006205 00646
## [1,] -0.0204115226 -0.015130674 -0.180168908 -0.0428500249
## [2,] 0.0292387834 0.042364991 -0.011827547 0.0005205622
## [3,] 0.0582857143 0.002233140 0.074517375 0.0265348595
## [4,] -0.0492132058 -0.038324421 -0.023715415 0.0045615813
## [5,] 0.0238520201 0.012974977 -0.002576371 0.0247225025
## [6,] 0.0339144216 0.032479414 -0.016974170 -0.0246184146
## [7,] 0.0554874310 0.080194949 0.012012012 0.0312973246
## [8,] 0.0124891083 0.013125513 0.024480712 0.0024473813
## [9,] 0.0192197361 0.013765182 -0.038015930 -0.0219726562
## [10,] 0.0153391500 -0.053913738 0.020700038 -0.0054917624
## [11,] -0.0059598060 -0.018151119 0.057522124 0.0411646586
## [12,] 0.0006971556 -0.008598452 -0.058577406 0.0385728062
## [13,] 0.0197854257 0.019947962 0.018148148 -0.0129990715
## [14,] -0.0012296762 0.039115646 0.003273918 0.0145813735
## [15,] 0.0103967168 0.013093290 -0.031182016 0.0000000000
## [16,] 0.0083942594 -0.012116317 -0.015344311 -0.0023180343
## [17,] 0.0296723953 0.015944399 0.035347777 0.0041821561
## [18,] 0.0445951232 0.027766600 0.060939794 0.0143452106
## [19,] 0.0154787168 0.021534847 0.043944637 0.0159671533
## [20,] 0.0195451752 0.000000000 0.038448790 -0.0094297261
## [21,] -0.0227875573 -0.006132618 -0.006383658 0.0385312783
## [22,] 0.0502159161 -0.015426147 0.024734982 0.0139676997
## [23,] -0.0300751880 -0.020368194 0.010344828 0.0111924236
## [24,] -0.0064195736 0.000000000 -0.004964319 0.0166028097
## [25,] 0.0295014019 0.053178729 0.082008107 0.0335008375
## [26,] -0.0197750148 -0.012528474 -0.036599424 -0.0113452188
## [27,] 0.0013288234 -0.001922338 -0.056536045 -0.0385245902
## [28,] -0.0384847388 -0.030046225 -0.016487001 0.0166240409
## [29,] 0.0127979925 0.040508340 -0.001611863 0.0310272537
## [30,] 0.0012388503 -0.023664122 -0.083629319 0.0166734445
## [31,] 0.0570403366 0.057857701 0.009866103 0.0340000000
## [32,] 0.0155683015 0.022172949 -0.038381019 0.0398452611
## [33,] 0.0051867220 -0.013738250 0.030478955 0.0000000000
## [34,] -0.1145510836 -0.130865103 -0.076408451 -0.0662202381
## [35,] -0.0012950013 0.032053986 0.001143729 0.0139442231
## [36,] -0.0247665975 -0.022476502 -0.038842346 -0.0899803536
etf4_ret.tmp<-data.frame(date = index(etf4_returns_xts), etf4_ret)
head(etf4_ret.tmp)
## date X0050 X0056 X006205 X00646
## 1 2016-01-31 -0.02041152 -0.01513067 -0.180168908 -0.0428500249
## 2 2016-02-29 0.02923878 0.04236499 -0.011827547 0.0005205622
## 3 2016-03-31 0.05828571 0.00223314 0.074517375 0.0265348595
## 4 2016-04-30 -0.04921321 -0.03832442 -0.023715415 0.0045615813
## 5 2016-05-31 0.02385202 0.01297498 -0.002576371 0.0247225025
## 6 2016-06-30 0.03391442 0.03247941 -0.016974170 -0.0246184146
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
p1 = plot_ly(etf4_ret.tmp, x= ~date, y= ~X0050, name = "0050", type = "scatter", mode = "lines") %>%
add_trace(y=~X0056, name = '0056',mode = 'lines+markers')%>%
layout(xaxis = list(title = 'year'), yaxis = list(title = 'monthly returns'))
p1
etf4_ret.df<-fortify(etf4_returns_xts, melt=TRUE)
head(etf4_ret.df)
## Index Series Value
## 1 2016-01-31 0050 -0.02062272
## 2 2016-02-29 0050 0.02881948
## 3 2016-03-31 0050 0.05665035
## 4 2016-04-30 0050 -0.05046543
## 5 2016-05-31 0050 0.02357200
## 6 2016-06-30 0050 0.03335201
p<-ggplot(etf4_ret.df, aes(x = Index, y = Value))+
geom_line(aes(color = Series), size = 1)
p + scale_x_date(date_labels = "%Y/%m")
q<-etf4_ret.df %>%
ggplot(aes(x =Value, fill = Series)) +
geom_histogram(alpha = 0.45, binwidth = .005) +
ggtitle("Monthly Returns")
q + facet_wrap(~Series)+ theme_update(plot.title = element_text(hjust = 0.5))
etf4_ret.df %>%
ggplot(aes(x = Value, colour = Series)) +
geom_density(alpha = 1) +
ggtitle("Monthly Returns Density Since 2016") +
xlab("monthly returns") +
ylab("distribution") +
theme_update(plot.title = element_text(hjust = 0.5))
etf4_ret.df %>%
ggplot(aes(x = Value)) +
geom_density(aes(color = Series), alpha = 1) +
geom_histogram(aes(fill = Series), alpha = 0.45, binwidth = .01) +
guides(fill = FALSE) +
facet_wrap(~Series) +
ggtitle("Monthly Returns Since 2016") +
xlab("monthly returns") +
ylab("distribution") +
theme_update(plot.title = element_text(hjust = 0.5))
